3D Convolutional Neural Networks for Video Recognition

Abstract

3D CNNs have proven to be an effective technique for analysing spatiotemporal data particularly in video recognition. By applying convolutions across consecutive video frames, 3D CNNs take into account both spatial and temporal dimensions, in contrast to typical 2D CNNs that process frames one at a time.

This makes the network extremely useful for comprehending motion and temporal patterns since it enables it to record both static visual information and the dynamic changes between frames. Robust computational resources, significant labelled video data, and advanced regularization techniques are necessary for the efficient training of 3D CNNs.

However, 3D CNNs' capacity to incorporate feature learning throughout time and space presents a number of advantages over conventional techniques, establishing them as a key technology in the advancement of video analysis skills. We have measured the efficiency of the classifier with various parameters accuracy, precision, recall, area under the ROC curve, mean average precision and loss metrics. The proposed classifier reports an accuracy of 98.64% which is promising.